The present invention relates generally to a high performance architecture for image processing.
There are many processor architectures which have been designed for image processing applications. In general, such applications have been implemented as a limited set of specific user functions. All image processing operations can be fully described by unary, binary, and matrix operations defined in an image algebra developed at the University of Florida. Furthermore, no known design has been built which supports a mathematically rigorous image processing language robust enough to express all common image processing operations.
The image/signal processing community at large is continuously seeking new software methods, hardware/firmware implementations, and processor architectures that provide higher throughput rates. The higher throughput allows for more comprehensive algorithms to be processed. This is particularly true for military applications (i.e., advanced guidance for autonomous weapons). Nearly all processing methodologies presently capable of being fielded (miniaturized and hardened) for military use have throughput ratings in term of millions of operations per second (Mega Ops). Even with these processing speeds certain image processing tasks cannot be accomplished within a systems total response time (i.e., the time necessary for a system to respond to a change in input information).
The following United States patents are of interest.
U.S. Pat. No. 4,967,340--Dawes
U.S. Pat. No. 4,850,027--Kimmel
U.S. Pat. No. 4,697,247--Grinberg et al
U.S. Pat. No. 4,601,055--Kent.
Dawes discloses an adaptive processing system for signal processing which includes a random access processor having an array of processing elements each being individually configurable. The latter appears to be the equivalent of a spatial configuration process component. Dawes appears to disclose an accumulation process component in the last paragraph of column 3 but appears to be lacking in any teaching of a point-wise operation process component. Kimmel is concerned with a computer system for image processing. The middle of column 5 of Kimmel discloses that the term "image" is used in the broadest sense, covering spatially or temporarily related information. The top of column 5 of Kimmel discusses the fact that the prior art discloses the use of image algebra in an image processor and that operations which do not involve the states of a pixel's neighbors may be performed in a separate point-by-point logic section to simplify the neighborhood logic circuit. Kimmel appears to be deficient in any teaching of an accumulation process component. Grinberg et al disclose a logical computer architecture for image processing which has means for representing spatially distributed data values, processing the data at every point in the image, and accumulating spatially distributed resultant values. Kent discloses an iconic-to-iconic image processor adapted to maintain spatial representation of images while performing a number of point and neighborhood operations. There is no discussion of an accumulation process component.